python - linear model ordinary least square vs Ridge -

i getting confused when reading scikit-learn documentation , trying compare logisticregression ridgeclassifier. both implementations compute least squares solution using singular value decomposition. while ridgeclassifier uses ridgeregression , therefore imposes l2 regularization, difference logisticregression when penalty set l2 default value